Uma Nova Meta-heurística Adaptativa Baseada em Vetor de Avaliações para Otimização de Portfólios de Investimentos
This article describes a new adaptive metaheuristic based on a vector evaluated approach for solving multiobjective problems. We called our proposed algorithm Vector Evaluated Meta-Heuristic. Its main idea is to evolve two populations independently, exchanging information between them, i.e., the first population evolves according to the best individual of the second population and vice-versa. The choice of which algorithm will be executed on each generation is carried out stochastically among three evolutionary algorithms well known in the literature: PSO, DE, ABC. In order to evaluate the results, we used an established metric in multiobjective evolutionary algorithms called hypervolume. Tests have shown that the adaptive metaheuristic reaches the best hyper-volumes in three of ZDT benchmarks functions and, also, in two portfolios of a real-world problem called portfolio investment optimization. The results show that our algorithm improved the Pareto curve when compared to the hypervolumes of each heuristic separately.
Copyright (c) 2019 Iberamia & The Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Inteligencia Artificial (Ed. IBERAMIA)
ISSN: 1988-3064 (on line).
(C) IBERAMIA & The Authors